1 citations · 2 across the 24 of their papers we have counts for
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TacitVLA: Reasoning Vision-Language-Action Models Without Reasoning Latency
Jack Lu, Yurong You, Boyi Li +8
Textual reasoning before acting has improved vision-language-action (VLA) models, but at the cost of latency: every action conditioned on the reasoning text must wait for autoregre…
SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models
Ahmadreza Jeddi, Enming Zhang, Jasper Gerigk +12
Reasoning vision-language models (VLMs) process images and videos as long sequences of visual tokens, making inference expensive. Training-free token pruning reduces this cost, but…
A Scene Language Model for Open-Vocabulary Scene Mapping
Adam Lilja, Fabio Hübel, Siming He +6
Open-vocabulary 3D scene mapping aims to build a persistent representation of the objects in an environment. Existing systems typically rely on engineered mapping pipelines to asso…
FARM: Find Anything using Relational Spatial Memory
Siming He, Leo Huang, Adam Lilja +6
Robots operating in homes, warehouses, and other object-rich environments need memory systems that can find specific object instances on demand. Object-level memory alone is often…
Towards Spatial Supersensing in the Wild
Tianjun Gu, Tianyu Xin, Kuan Zhang +12
Humans can efficiently parse continuous sensory streams, from hours to years, scaffolding an internal world model that grounds spatial reasoning and prediction. To mimic this capac…
WildCity: A Real-World City-Scale Testbed for Rendering, Simulation, and Spatial Intelligence
Xiangyu Han, Mengyu Yang, Jiaqi Li +9
Humans can navigate an unfamiliar city and gradually form a coherent spatial mental map spanning tens of square kilometers. Can AI build spatial representations at a comparable sca…
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
Planning-aligned Token Compression for Long-Context Autonomous Driving
Zhixuan Liang, Yuxiao Chen, Yurong You +12
Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly exceed real-time computationa…
Fast-dDrive: Efficient Block-Diffusion VLM for Autonomous Driving
Kewei Zhang, Jin Wang, Sensen Gao +9
End-to-end autonomous driving via Vision-Language-Action (VLA) models demands a precarious balance between high-fidelity trajectory planning and efficient inference. Existing parad…
Bridging Structure and Language: Graph-Based Visual Reasoning for Autonomous Road Understanding
Lena Wild, Katie Z Luo, Marco Pavone
Structured road understanding of lane geometry, topology, and traffic element relationships is foundational to safe autonomous driving. While vision-language models (VLMs) offer pr…
VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence
Mozhgan Nasr Azadani, Yimu Wang, Yongpeng Zhu +5
Establishing a clear link between model predictions and the visual evidence that supports them is critical for transparency and reliability in multimodal reasoning, yet current mul…
: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation
Songlin Wei, Hongyi Jing, Boqian Li +12
We introduce (Psi-Zero), an open foundation model to address challenging humanoid loco-manipulation tasks. While existing approaches often attempt to address this fundamental…
Accelerating Structured Chain-of-Thought in Autonomous Vehicles
Yi Gu, Yan Wang, Yuxiao Chen +8
Chain-of-Thought (CoT) reasoning enhances the decision-making capabilities of vision-language-action models in autonomous driving, but its autoregressive nature introduces signific…
TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs
Chenchen Lin, Sanbao Su, Rachel Luo +4
Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utili…